A Novel Graphic-Aided Algorithm (gNIPT) Improves the Accuracy of Noninvasive Prenatal Testing

• Main Authors: Qingwen Zhu, Jing Wang, Xiaoning Xu, Shiying Zhou, Zhengli Liao, Jun Zhang, Lingyin Kong, Bo Liang, Xiaoyan Cheng
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: BioMed Research International
• Online Access: http://dx.doi.org/10.1155/2020/4712657

Noninvasive Prenatal Testing (NIPT) has advanced the detection of fetal chromosomal aneuploidy by analyzing cell-free DNA in peripheral maternal blood. The statistic Z-test that it utilizes, which measures the deviation of each chromosome dosage from its negative control, is now widely accepted in clinical practice. However, when a chromosome has loss and gain regions which offset each other in the z-score calculation, merely using the Z-test for the result tends to be erroneous. To improve the performance of NIPT in this aspect, a novel graphic-aided algorithm (gNIPT) that requires no extra experiment procedures is reported in this study. In addition to the Z-test, this method provides a detailed analysis of each chromosome by dividing each chromosome into multiple 2 Mb size windows, calculating the z-score and copy number variation of each window, and visualizing the z-scores for each chromosome in a line chart. Data from 13537 singleton pregnancy women were analyzed and compared using both the normal NIPT (nNIPT) analysis and the gNIPT method. The gNIPT method had significantly improved the overall positive predictive value (PPV) of nNIPT (88.14% vs. 68.00%, p=0.0041) and the PPV for trisomy 21 (T21) detection (93.02% vs. 71.43%, p=0.0037). There were no significant differences between gNIPT and nNIPT in PPV for trisomy 18 (T18) detection (88.89% vs. 63.64%, p=0.1974) and in PPV for trisomy 13 (T13) detection (57.14% vs. 50.00%, p=0.8004). One false-negative T18 case in nNIPT was detected by gNIPT, which demonstrates the potency of gNIPT in discerning chromosomes that have variation in multiple regions with an offsetting effect in z-score calculation. The gNIPT was also able to detect copy number variation (CNV) in chromosomes, and one case with pathogenic CNV was detected during the study. With no additional test requirement, gNIPT presents a reasonable solution in improving the accuracy of normal NIPT.